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  • Writer's pictureDr. Bohdan Tanyhin

Natural Language Processing (NLP) and Natural Language Understanding (NLU)

Natural languages arose as the perfect means of communication and mutual understanding. These are English, German, French, Italian, and the other 7,139 languages in the world. However, concerning technologies, we have artificially created languages that help us communicate with and become understandable by computers. These are Java, C, Python, JavaScript, etc., which are programming languages, technical, existing as code.

With technological progress, a need to process and understand human language through computers became a huge necessity. The ability to analyze, assess, and comprehend human language becomes possible with the help of Artificial Intelligence (AI). More specifically, with the help of such AI branches as Natural Language Processing (NLP) and Natural Language Understanding (NLU).


To understand the specificity of NLP and NLU, let’s discuss each concept separately. Sencury has some expertise to share. Let’s proceed.


What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is the branch of computer science and Artificial Intelligence. Its basic aim is to make human speech and text as comprehensible as possible for computers (machines). To process human language, computers utilize computational linguistics and statistical language models. The first one is the rule-based modeling of human language, and the second includes machine learning (ML) and deep learning (DL).


NLP gained much popularity during the last few years and is heavily invested in today. Therefore, Statista projects that by 2028, its market value will exceed $127 billion.

NLP gained much popularity during the last years and is heavily invested in today. Therefore, Statista projects that by 2028, its market value will exceed $127 billion.

How Does NLP Work?

NLP undergoes two main processes – data preprocessing and the development of algorithms. Here, data preprocessing is the process of “cleaning” data for machines to be able to analyze it. Data preprocessing can be done in several different ways:

  • Tokenization, e.g., the text is broken down into smaller pieces for the machine to work with it.

  • Stop word removal, i.e., removal of the common words in the text so that the remaining unique words in the text could bring the most value.

  • Lemmatization and stemming, e.g., extracting the stems of the words for easier processing by the machine.

  • Part-of-speech tagging, i.e., marking the words according to their corresponding part of speech (verbs, nouns, adjectives, adverbs, etc.).

NLP undergoes two main processes – data preprocessing and the development of algorithms. Here, data preprocessing is the process of “cleaning” data for machines to be able to analyze it. Data preprocessing can be done in several different ways:   Tokenization, e.g., the text is broken down into smaller pieces for the machine to work with it.    Stop word removal, i.e., removal of the common words in the text so that the remaining unique words in the text could bring the most value.   Lemmatization and stemming, e.g., extracting the stems of the words for easier processing by the machine.    Part-of-speech tagging, I.e., marking the words according to their corresponding part of speech (verbs, nouns, adjectives, adverbs, etc.).

The data preprocessing is followed by algorithm development. The duty of algorithms is to process the data obtained after it is cleaned out. In a variety of algorithms that exist, there are two common ones that need to be mentioned. These are:

  • “Rules-based” system. It is a system based on carefully designed linguistic rules.

  • “Machine learning-based" system. ML algorithms work using statistical methods. Algorithms need training data to be input for them to learn on it. The more data they are trained on the better the output will be. Therefore, this kind of system uses a combination of ML, DL, and Neural networks (a computer system modeled on the human brain and nervous system).

Techniques and Methods of Natural Language Processing

NLP utilizes techniques such as syntax and semantic analysis. Syntax sets up the grammatical sense in the sentence. It denotes the correct arrangement of words and NLP uses grammatical rules to assess the meaning of a language. Syntax techniques include:

  • Parsing – grammatical analysis of a sentence with breaking it into different parts of speech.

  • Word segmentation – deriving words in specific forms for the algorithm to recognize these words during the analysis of the webpage.

  • Sentence breaking – using dots, commas, semi-colons, and other punctuation to let the algorithm know where the sentence ends.

  • Morphological segmentation – dividing words into morphemes (smaller parts) to make them more comprehensible for speech recognition and machine translation.

  • Stemming – extracting the stem of the words, or its initial root for the algorithm to understand that the words are the same notwithstanding the different conjugations and letters.

Concerning semantics, it denotes the use of words' meanings. NLP needs algorithms to understand both the meaning and the structure of sentences. That’s why there are semantic techniques involved:

  • Word sense disambiguation – extracting the meaning of the word in a context.

  • Named entity recognition - determining words that can form groups based on categories.

  • Natural language generation – via a database with the word meaning the machine can understand what the text means and generate the new one.

What is Natural Language Processing Used For?

Nowadays, there are lots of applications of NLP businesses exploit to derive the most benefit. For example,

  • Classifying text

  • Extracting text

  • Performing translation by a machine

  • Generating natural language

All these possibilities are being used to:

  • Analyse social media reviews for customer feedback;

  • Understand voice via speech recognition and automate customer service;

  • Translate any text into any language automatically via Google Translate, etc.;

  • Perform academic research by analyzing piles of academic materials;

  • Analyze and categorize medical records to predict and prevent diseases;

  • Process text (specifically, words) for plagiarism and proofread (e.g., Grammarly, MS Word, etc.);

  • Forecasting stocks and gaining insights into financial trading via analyzing company documentation history;

  • Recruit talents by human resources tools;

  • Automate routine-driven litigation tasks via AI attorney

This is just a simple list of common applications. AI can be applied to almost every sphere of life, and it makes this technology unique and usable.

Benefits of Natural Language Processing (NLP)

The biggest benefit NLP can provide is the ability for “human-computer” interaction. With NLP, machines can understand different language variations more accurately. Among the other benefits you might find:

  • Documentation efficiency and accuracy;

  • Automatic text summary notwithstanding the original text size;

  • Helpful for Alexa, Siri, Bixby, and other personal assistants that interpret voice commands;

  • Chatbot usage for organizations providing customer support;

  • Performing sentiment analysis better;

  • Provision of personal analytics insights on any data volume.

What is Natural Language Understanding (NLU)?

Natural language understanding (NLU) is also a branch of AI. Its main goal is to understand human input either in the form of a text or speech. So, it makes “human-computer” interaction possible as well. Unlike NLP, NLU does not only process and understand human languages, but can also provide answers in the language it was addressed in. Therefore, the main purpose of NLU is to create interactive chatbots that will help the public with their requests. Amazon, Google, Microsoft, Apple, and other startups work on NLU projects and offer NLU innovations daily.

How Does Natural Language Understanding Work?

NLU puts human speech into a structured ontology. The latter is a semantics and pragmatics data model. Therefore, the algorithms trained on the current data model can understand natural language and determine its meaning. Also, NLU is based on the following concepts:

  • Intent recognition – identification of human emotional state in the text and understanding of its goal. This way the meaning of the text is being established.

  • Entity recognition – extraction of entities in the message and finding the most important data about those entities. There are two types of entities: named (people, locations, and companies) and numeric (numbers, currencies, and percentages).

More of the Artificial General Intelligence (AGI) NLU concepts will be described in our future blogs related to Large Language Models (LLMs).

Natural Language Understanding Applications

There are a variety of NLU applications. However, the most common today are

  • IVR and message routing

  • Customer support service through personal AI assistants

  • Machine translation

  • Data capture

  • Conversational interfaces

Sencury Offers NLP and NLU Services

The development of AI capabilities goes further, and new creative possibilities arise. Therefore, Sencury is your top AI and ML services provider. Our AI-savvy team will provide you with quality

  • Natural Language Processing

  • Computer Vision

  • Neural Networks

  • Cognitive Computing

  • Deep Learning

  • ML Model Development

  • Predictive Analytics

  • Chatbots Development

  • Data Engineering

  • Data Analysis

  • Data Mining

  • Marketing Automation Solutions

Artificial Intelligence allows businesses to acquire automation and learn about user data. Therefore, we can help you to meet your business goals. Sencury’s expertise includes top industry professionals, the best toolset, and creative approaches among the other set standards. Start your NLP project together with us! Contact our AI engineers for the details!


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